MétaCan
Menu
Back to cohort
Record W2902402004 · doi:10.5539/ijel.v8n7p7

Tragedy and the Tragic: A Study of Ernst Hemingway’s A Farewell to Arms and Naguib Mahfouz’s The Beginning and the End

2018· article· en· W2902402004 on OpenAlexvenueno aff
Rania Khelifa Chelihi, Mohd Nazri Latiff Azmi, Hardev Kaur, Ayaicha Somia

Bibliographic record

VenueInternational Journal of English Linguistics · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican and British Literature Analysis
Canadian institutionsnot available
FundersThamar University
KeywordsTragedy (event)HegelianismLiteraturePhilosophyOrder (exchange)Greek tragedyExcellenceEpistemologyArt

Abstract

fetched live from OpenAlex

This paper is a comparative study between Ernst Hemingway’s A Farewell to Arms and Naguib Mahfouz’s The Beginning and the End, paralleled with the authors’ concepts of tragic vision; based on the development of the theory of tragedy from Aristotle to Hegel as well as the personal philosophy of life as tragedy of both authors. Based on the researcher knowledge, tragedy concept in the selected novels is rarely and insufficiently highlighted by few scholars and critics. Moreover, it is a comparison of novels from different cultures—Arabic literature and literature in English—in order to bridge the gap between them. The novels are stories where every day moral dilemmas often present profound paradoxes with which heroes and heroines must deal. Tragedy, in the same vein, is such a paradoxical story where we have to deal at any rate with our everyday moral dilemmas, where we are sometimes called upon to make difficult choices not between right and wrong, but between what we might define as two rights. Hegelian concept of tragedy focused on dissension and war of dichotomies between good and bad, as well as what is right and what is wrong. The tragic elements in the two novels make them Hegelian tragedies par excellence.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.244
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicAmerican and British Literature AnalysisFrench-language works237,207